@InProceedings{henderson-EtAl:2017:SemEval,
  author    = {Henderson, John  and  Merkhofer, Elizabeth  and  Strickhart, Laura  and  Zarrella, Guido},
  title     = {MITRE at SemEval-2017 Task 1: Simple Semantic Similarity},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {185--190},
  abstract  = {This paper describes MITRE's participation in the Semantic Textual Similarity
	task (SemEval-2017 Task 1), which evaluated machine learning approaches to the
	identification of similar meaning among text snippets in English, Arabic,
	Spanish, and Turkish. We detail the techniques we explored ranging from simple
	bag-of-ngrams classifiers to neural architectures with varied attention and
	alignment mechanisms. Linear regression is used to tie the
	systems together into an ensemble submitted for evaluation. The resulting
	system is capable of matching human similarity ratings of image captions with
	correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in
	cross-lingual conditions, demonstrating the power of relatively simple
	approaches.},
  url       = {http://www.aclweb.org/anthology/S17-2027}
}